<p>Recent years have witnessed significant interest in drone positioning due to the rapid development of aerial surveillance, autonomous navigation, and intelligent monitoring applications. Although deep learning models achieve high drone detection accuracy, most existing systems lack an integrated vision-based framework for estimating the real-world three-dimensional position of drones. This paper suggests a drone positioning system that incorporates a deep learning detection system with stereo-vision-based localization in order to overcome this limitation. To train a YOLOv8 detection model, the UAVfly dataset of 1005 annotated drone images is used. Images are all resized to 640 640 pixels and also normalized to facilitate constant training. Once the drone is detected, the predicted bounding boxes are used to extract the corresponding regions of interest from the stereo image pairs. The SURF (Speeded-Up Robust Features) algorithm is utilized to extract the features and match them in order to detect significant points in the chosen region of the images. A brute-force matcher is then used to match these features of the left and right stereo images to get the correct corresponding points. On these matched points, depth is computed from disparity. Lastly, a 3D position of the drone is calculated by averaging the coordinates of several matched feature points, and this allows for more precise localization. The accuracy, precision, recall, F1-score, intersection over union, and mean average precision of the proposed method are 0.930, 0.942, 0.933, 0.938, 0.873, and 0.934, respectively. These findings confirm the effectiveness of the proposed method for accurate drone detection and reliable three-dimensional localization.</p>